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Chen Z, Liang B, Wu Y, Zhou H, Wang Y, Wu H. Identifying driver modules based on multi-omics biological networks in prostate cancer. IET Syst Biol 2022; 16:187-200. [PMID: 36039671 PMCID: PMC9675413 DOI: 10.1049/syb2.12050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 07/31/2022] [Accepted: 08/13/2022] [Indexed: 01/11/2023] Open
Abstract
The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi-omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors' method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.
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Affiliation(s)
- Zhongli Chen
- Tibet Center for Disease Control and PreventionLhasaChina
- School of SoftwareShandong UniversityJinanChina
- School of Information EngineeringNorthwest A&F UniversityYanglingChina
| | - Biting Liang
- School of Information EngineeringNorthwest A&F UniversityYanglingChina
| | - Yingfu Wu
- School of Information EngineeringNorthwest A&F UniversityYanglingChina
| | - Haoru Zhou
- School of Information EngineeringNorthwest A&F UniversityYanglingChina
| | - Yuchen Wang
- School of SoftwareShandong UniversityJinanChina
| | - Hao Wu
- School of SoftwareShandong UniversityJinanChina
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Jiang L, Wang P, Su M, Yang L, Wang Q. Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma. Front Genet 2022; 13:880945. [PMID: 35664306 PMCID: PMC9159392 DOI: 10.3389/fgene.2022.880945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated. Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival. Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.
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Affiliation(s)
- Linlin Jiang
- Department of Chemotherapy, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Peng Wang
- Department of General Surgery, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Mu Su
- Department of Chemotherapy, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Lili Yang
- Department of Chemotherapy, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Qingbo Wang
- Department of Chemotherapy, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
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Gao B, Zhao Y, Gao Y, Li G, Wu L. Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis. GLOBAL CHALLENGES (HOBOKEN, NJ) 2021; 5:2100006. [PMID: 34504716 PMCID: PMC8414517 DOI: 10.1002/gch2.202100006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/26/2021] [Indexed: 05/12/2023]
Abstract
High-throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivity-coverage iteration strategy and outputting those with significant coverage and exclusivity for both cancers. The associations of the cancer pairs are further evaluated by Fisher's exact test. Being applied to 11 TCGA cancer datasets, ComCovEx identifies 13 significantly associated cancer pairs with plenty of biologically significant common gene modules. The novel results of cancer relationship and common gene modules reveal the relevant pathological basis of different cancer types and provide new clues to diagnosis and drug treatment in associated cancers.
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Affiliation(s)
- Bo Gao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of MathematicsShandong UniversityJinan250100China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
- School of Public HealthCapital Medical UniversityBeijing100069China
- Beijing Municipal Key Laboratory of Clinical EpidemiologyBeijing100069China
| | - Yue Zhao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Yonghang Gao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Guojun Li
- School of MathematicsShandong UniversityJinan250100China
- Research Center for Mathematics and Interdisciplinary SciencesShandong UniversityQingdao266237China
| | - Ling‐Yun Wu
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
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Fedrizzi T, Ciani Y, Lorenzin F, Cantore T, Gasperini P, Demichelis F. Fast mutual exclusivity algorithm nominates potential synthetic lethal gene pairs through brute force matrix product computations. Comput Struct Biotechnol J 2021; 19:4394-4403. [PMID: 34429855 PMCID: PMC8369001 DOI: 10.1016/j.csbj.2021.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 12/12/2022] Open
Abstract
Mutual Exclusivity analysis of genomic aberrations contributes to the exploration of potential synthetic lethal (SL) relationships thus guiding the nomination of specific cancer cells vulnerabilities. When multiple classes of genomic aberrations and large cohorts of patients are interrogated, exhaustive genome-wide analyses are not computationally feasible with commonly used approaches. Here we present Fast Mutual Exclusivity (FaME), an algorithm based on matrix multiplication that employs a logarithm-based implementation of the Fisher's exact test to achieve fast computation of genome-wide mutual exclusivity tests; we show that brute force testing for mutual exclusivity of hundreds of millions of aberrations combinations can be performed in few minutes. We applied FaME to allele-specific data from whole exome experiments of 27 TCGA studies cohorts, detecting both mutual exclusivity of point mutations, as well as allele-specific copy number signals that span sets of contiguous cytobands. We next focused on a case study involving the loss of tumor suppressors and druggable genes while exploiting an integrated analysis of both public cell lines loss of function screens data and patients' transcriptomic profiles. FaME algorithm implementation as well as allele-specific analysis output are publicly available at https://github.com/demichelislab/FaME.
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Affiliation(s)
- Tarcisio Fedrizzi
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Yari Ciani
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Francesca Lorenzin
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Thomas Cantore
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Paola Gasperini
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Al-Saud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10021, USA
- The Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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Fang H, Zhang Z, Zhou Y, Jin L, Yang Y. A greedy approach for mutual exclusivity analysis in cancer study. Biostatistics 2021; 23:910-925. [PMID: 33634822 DOI: 10.1093/biostatistics/kxab004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 11/26/2020] [Accepted: 01/13/2021] [Indexed: 11/14/2022] Open
Abstract
The main challenge in cancer genomics is to distinguish the driver genes from passenger or neutral genes. Cancer genomes exhibit extensive mutational heterogeneity that no two genomes contain exactly the same somatic mutations. Such mutual exclusivity (ME) of mutations has been observed in cancer data and is associated with functional pathways. Analysis of ME patterns may provide useful clues to driver genes or pathways and may suggest novel understandings of cancer progression. In this article, we consider a probabilistic, generative model of ME, and propose a powerful and greedy algorithm to select the mutual exclusivity gene sets. The greedy method includes a pre-selection procedure and a stepwise forward algorithm which can significantly reduce computation time. Power calculations suggest that the new method is efficient and powerful for one ME set or multiple ME sets with overlapping genes. We illustrate this approach by analysis of the whole-exome sequencing data of cancer types from TCGA.
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Affiliation(s)
- Hongyan Fang
- School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
| | - Zeyu Zhang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China
| | - Yinsheng Zhou
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China
| | - Lishuai Jin
- School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
| | - Yaning Yang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China
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Gu H, Xu X, Qin P, Wang J. FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network. Front Genet 2020; 11:564839. [PMID: 33244318 PMCID: PMC7683798 DOI: 10.3389/fgene.2020.564839] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 09/30/2020] [Indexed: 12/24/2022] Open
Abstract
Identification of driver genes, whose mutations cause the development of tumors, is crucial for the improvement of cancer research and precision medicine. To overcome the problem that the traditional frequency-based methods cannot detect lowly recurrently mutated driver genes, researchers have focused on the functional impact of gene mutations and proposed the function-based methods. However, most of the function-based methods estimate the distribution of the null model through the non-parametric method, which is sensitive to sample size. Besides, such methods could probably lead to underselection or overselection results. In this study, we proposed a method to identify driver genes by using functional impact prediction neural network (FI-net). An artificial neural network as a parametric model was constructed to estimate the functional impact scores for genes, in which multi-omics features were used as the multivariate inputs. Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. According to the comprehensive evaluation criterion, FI-net was powerful among various datasets and outperformed the other methods in terms of the overlap fraction with Cancer Gene Census and Network of Cancer Genes database, and the consensus in predictions among methods. Furthermore, the results illustrated that FI-net can identify known and potential novel driver genes.
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Affiliation(s)
- Hong Gu
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Xiaolu Xu
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Pan Qin
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Jia Wang
- Department of Breast Surgery, Institute of Breast Disease, Second Hospital of Dalian Medical University, Dalian, China
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Association extraction from biomedical literature based on representation and transfer learning. J Theor Biol 2020; 488:110112. [DOI: 10.1016/j.jtbi.2019.110112] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 12/08/2019] [Indexed: 12/17/2022]
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